Front. Agric. China 2011, 5(4): 504–513 DOI 10.1007/s11703-011-1102-6
RESEARCH ARTICLE
Physical land suitability evaluation for specific cereal crops using GIS at Mashhad Plain, Northeast of Iran Ali BAGHERZADEH ( 1 2
✉)1, Mohammad Reza MANSOURI DANESHVAR2
Department of Agriculture, Mashhad Branch, Islamic Azad University, Mashhad, Iran Department of Geography, Mashhad Branch, Islamic Azad University, Mashhad, Iran
© Higher Education Press and Springer-Verlag Berlin Heidelberg 2011
Abstract Land evaluation is the process of predicting land use potential on the basis of its attributes. In the present study, the physical land suitability evaluation approach was investigated for specific cereal crops including irrigated wheat, barley, grain maize and sorghum, based on FAO land evaluation frameworks (FAO, 1976, 1983, 1985) and the proposed method by Sys et al. (1991) at Mashhad Plain, Northeast Iran. Twenty eight soil profiles were studied on seven land units by a precise soil survey and their morphological and physicochemical properties were determined. Climatic and land qualities/characteristics of four cereal crops were determined using the tables of crop requirements developed by Sys et al. (1993). An interpolation function was used to map values to scores in terms of land qualities/characteristics for land utilization types and the evaluation was carried out according to parametric approaches. The interpolation technique using GIS functions helped in managing the spatial data and visualizing the results. Our results indicated that the most important limiting factors for irrigated wheat and barley cultivations are soil physical and fertility/chemical properties, while the production of irrigated grain maize and sorghum is mainly limited by climatic conditions at Mashhad Plain. It was shown that all land units suitable for irrigated wheat cultivation overlap with that of barley, whereas the same limiting factors resulted in the overlapping of the irrigated grain maize production area with that of sorghum. The results of the physical land suitability evaluation for specific cereal crops indicated the priority of irrigated barley and wheat cultivations over irrigated grain maize and sorghum at the study area. Keywords
land suitability evaluation, parametric methods, Storie and Square root methods, Kalogirou method
Introduction Land evaluation (FAO, 1976) is defined as “the process of assessment of land performance when used for specific purposes...” The FAO land evaluation framework (1976) has been the primary procedure employed worldwide to address local, regional, and national land use planning. In recent years, computing technologies combined with GIS software have enabled a countless number of reports and studies using the land evaluation FAO framework for addressing old and new challenges especially at the regional scale. Despite several limitations, FAO soils bulletins 32 and 55 for land evaluation (1976, 1985) still play a major role in civil society,
Received February 15, 2011; accepted March 1, 2011 Correspondence: Ali BAGHERZADEH E-mail:
[email protected]
most notably outside the strict borders of agriculture. One obvious reason for its widespread use is the straightforward approach of the procedure, which applies simple models. In fact, the classical land evaluation approach is based on qualitative models that require only a basic structural knowledge of the specific land and object of evaluation (e.g. crops). A qualitative classification is commonly employed in reconnaissance surveys for general planning purposes based mainly on the physical productive potential of the land with relative suitability expressed in qualitative terms only, without the precise calculation of costs and returns. The problem of selecting the correct land for the cultivation of a certain agriculture product is a long-standing and mainly empirical issue. Although many researchers and organizations have tried to provide a framework for optimal agricultural land use, it is suspected that much agricultural land is used at below its optimal capability. The increased need for food production and the shortage of resources stimulate a need for sophisticated methods of land evaluation
Ali BAGHERZADEH and Mohammad Reza MANSOURI DANESHVAR
to aid decision-makers in their role to both preserve highly suitable lands and satisfy producers’ demands for increasing profits. In fact, land suitability evaluation is an examination process of the degree of land suitability for a specific utilization type (Sys et al., 1991a) anda description method or estimation of potential land productivity (Rossiter, 1996). The climate of Iran is one of great extremes because of its geographical location and topography. Most of the country is arid to semiarid with an average annual precipitation of about 250 mm, whereas it ranges from 550 to 1800 mm in the northern flanks of the Alburz mountain chains. Therefore, in arid and semi-arid areas of the country with an extent of 3.25 107 hm2, the soils have a low production potential because of the restricted rainfall. Suitable land change after degradation by salinity or alkalinity requires suitable land use. A part of the problem can be solved by land evaluation leading to rational land use planning FAO (1976), appropriate and sustainable use of natural and human resources and by optimizing the use of a piece of land for a specified use (Sys et al., 1991a). Kalogirou (2002) suggested a classification method with very specific requirements for classifying land that is more logical than any of the other parametric methods (Storie and Square root methods), so that those class of determining factors that are highly suitable require the minimum input during the cultivation process. The methodology adopted involves most aspects of climatic and soil requirements (including soil physical properties, soil fertility and chemical properties, soil salinity and alkalinity, topography, erosion hazard and wetness) for each crop (Sys et al., 1991a, 1991b, 1993). Sokol et al. (2004) used the parametric square root method for qualitative land suitability evaluation in Tunisia for wheat, barley, sorghum, potato, etc. Njiki et al. (2005) performed a land evaluation project for Shouyang County in Shanxi Province, China, in which maize, soybean, potato, sunflower, wheat, and free crops were studied. For this purpose, land suitability classification was carried out using the parametric method and the consequent land suitability maps were prepared for crops under traditional and mechanized cultivations. Dunshan et al. (2006) investigated the land suitability for agricultural crops in Danling County of Sichuan Province, China, using the Sys parametric evaluation system. Jafarzadeh and Abbasi (2006) assessed the qualitative physical land suitability evaluation for the growth of onion, potato, maize, and alfalfa on soils at a research station in Azerbaijan Province, Northwest Iran and Behzad et al. (2009) evaluated the qualitative land suitability for principal crops based on parametric methods at Khuzestan Province, Southwest Iran. Kalogirou (2002) combined expert systems and geographical information systems technologies to help with an implementation of a land suitability evaluation model. This paper applies FAO’s framework (1976, 1983, 1985) for the physical land suitability evaluation based on parametric approaches. The aim of the present study was to evaluate and compare qualitative land suitability for major cereal crops (including irrigated wheat, barley, grain maize and sorghum)
505
based on parametric Storie, Square root and Kalogirou evaluation methods at Mashhad Plain, Northeast Iran.
Materials and methods General characteristics of the study area The present study was conducted in Mashhad Plain with an area of 6131 km2, Khorasan-e-Razavi Province, Northeast Iran (Fig. 1).
Figure 1 Location and geographical position of the study area.
The study area is located between latitude 35°59′N to 37°04′N and longitude 58°22′E to 60°07′E including lands less than 1500 m ASL. The general physiographic trend of the plain extends in a NW-SE direction with an average of 160 km in length surrounded by the two mountainous zones of Kopet-dagh northward and Binaloud southward based on a visual interpretation of the satellite image and field observations (Fig. 2). The topographical elevation values of the study area vary between 900 m ASL and 1500 m ASL, while the main topographical elevation ranges over 1200 m ASL (Fig. 3).
Figure 2
Satellite image of the study area.
506
Physical land suitability evaluation for specific cereal crops using GIS
Physical land suitability procedure The basis of the present methodology lies in the traditional qualitative land evaluation, and land qualities/characteristics are matched with each specific crop requirements in order to find the suitability class of land for the same crop (FAO, 1976). The methodology comprises two key steps: Step 1 is to identify land units with a similar topography and soil conditions (Fig. 5), of which topography is described by the global 30-arc second digital elevation model (DEM).
Figure 3
Topography and elevation map of the study area.
Geologically, the main alluvial nature of the plain has developed into a thick sediment-dominated environment belonging to the quaternary period. The main soil textures are loam, sandy loam and sandy clay loam. The dominant soil types include calcaric cambisols, gypsic regosols, calcaric regosols and calcaric fluvisols which cover pediment plains, plateaus and upper terraces and gravelly colluvial fans, respectively. In order to have confident soil data at the study area, 28 soil profiles were investigated. The soil orders were classified based on the USDA classification system (USDA, 2003) as Aridisols and Entisols. The study area consisted of 6 cities with a population of about 2481290 and 519 villages with a population of about 422610, scattered over the plain. The main land use practiced at the study area is irrigated farming around the Kashaf-roud River, with a semi-arid climate, mean annual precipitation of 222.1 mm and mean annual temperature of 15.8°C. The rainiest month is March (44.8 mm) and the driest month is September (1.2 mm). The soil profiles are shown in Fig. 4.
Figure 4 Soil profiles location map of the study area.
Figure 5
Land units map of the study area.
Step 2 is to match the properties of the land units with crop requirements including the traditional matching process, as described in the FAO qualitative land evaluation system (FAO, 1976, 1983, 1985) used to compare land qualities/ characteristics of topography, erosion hazard, wetness, soil physical properties, soil fertility and chemical properties, soil salinity and alkalinity with each specific crop requirements developed by Sys et al. (1991a, 1991b, 1993). The physical land suitability evaluation indicates the degree of suitability for land use, without respect to economic conditions, emphasizing the relatively permanent aspects of suitability, such as climate and soil qualities/characteristics, rather than changeable ones, such as prices. It tends to concentrate on risks or hazards, e.g., to the environment, or absolute limitations, e.g., due to climate, of undertaking a given land utilization type on a given land area. The idea is that if land use is too risky or physically impossible, no economic analysis can justify it. For the completely unsuitable land, the physical land suitability evaluation can be used to divide the land according to the degrees of suitability, based purely on physical conditions. The advantage is that physical suitability does not change quickly. Additionally, the physical land suitability evaluation can also be used to divide the land units into management groups. In this case, the physical suitability subclass designation shows the relative severity of the various limitations to use and their
Ali BAGHERZADEH and Mohammad Reza MANSOURI DANESHVAR
types. For example, the subclass ‘3c’ might indicate that the rated land unit has moderate limitations (‘3’) for the rated land use, with the main limitation being due to climate (‘c’). Parametric approaches in physical land suitability evaluation The physical land suitability evaluation consists of a model that assigns a score to every land quality and characteristic. Land quality is a complex attribute of land, which in a distinct manner influences its suitability for a specific kind of use, while land characteristics are any measurable features of land that can be used to characterize a land unit. Boolean classification is implemented in such a way that a higher score of class is given (e.g. 85) to classified (qualitative) values (e.g. Soil texture/structure = SL), while a linear interpolation function is used to assign a score to continues (quantitative) values. The data from a soil survey are often continuous data and therefore it is necessary to apply a classification scheme that assigns scores to individual land qualities/characteristics based on linear interpolation functions from map value intervals to score intervals. If the observed value is x and falls into the interval [a and b] it needs to get a score y that falls into the interval [c and d]. The calculation is performed using the following formula: ðb – aÞðx – cÞ y¼aþ : d–c
(1)
Each class-determining factor is first matched individually. Critical limits indicate the suitability level of a land unit for a given Land utilization type (LUT) in terms of that factor. For example, if one of the class-determining factors for the LUT “Irrigated Maize” is ‘Rooting’ (i.e. the requirements or limitations for root development), the critical limits are to be represented in terms of ‘effective soil depth’ corresponding to suitability levels of S1, S2, S3, N1 and N2 respectively. The recorded effective soil depth of each land unit will fall within one of these five depth ranges and the appropriate one is selected as the factor rating (Tables 1 and 2). Table 1 Land suitability classes according to degree of limitations (Sys et al., 1991) Soil class
Intensity of limitation
Degree of limitation
S1
Without limitations
95–100
S1
Slight limitations
85–95
S2
Moderate limitations
60–85
S3
Severe limitations
40–60
N1
Very severe (modifiable)
25–40
N2
Very severe (non modifiable)
0–25
In combining the factor ratings of several individual factors in order to decide the appropriate land suitability class to assign, the possibility of interactions should be taken into account. In a broad interpretation of the meaning of the word
Table 2 1991)
507 Land suitability classes according to land index (Sys et al.,
Soil class
Intensity of limitation
Land index
S1
Highly suitable
75–100
S2
Moderately suitable
50–75
S3
Marginally suitable
25–50
N1
Currently not suitable
12.5–25
N2
Permanently not suitable
0–12.5
‘interaction’ it can be readily appreciated that many factors interact in the resultant land index which is the integral of their effects. In order to classify the land qualities, parametric methods (Storie, Square root and Kalogirou methods) were implemented in the present study. Climate evaluation Climate data related to different stages of plant growth were taken from ten years of meteorological data of the Mashhad Synoptic Station (1997–2006) and the climatic requirements of each crop were extracted from the tables developed by Sys et al. (1993). Based on crop climatic requirements, the climatic index (CI), climatic rate (CR) and class for each crop were determined as important factors in estimating final land index. Storie method According to the Storie (1976) equation, land index is calculated based on the multiplication of different ratings given to land characteristics in the climatic rating as follows: I ¼A
B C D , 100 100 100
(2)
where, I is land index and A, B, C... are the ratings given to climate and each land characteristic. This method reflects the results as usually weaker than in reality. For instance, if the rating of four land characteristics is 90, the rating of the rest will be 100 with the final rating of 65.6, which seems to be unrealistic, because the multiplication is continuously done in ratings smaller than one. Hence, because of the interaction of many-sided impacts of land properties, using the Storie method in determining the land index will lead to underestimation of the obtained land classes. Square root method Based on the Samir equation (Samir, 1986), the calculated land index exhibits, to some extent, the interactions between land qualities and characteristics which is more logical than the Storie method. rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A B C I ¼ Rmin , (3) 100 100 100 where, I is land index, Rmin is the minimum rating among different land qualities and characteristics and A, B, C... are the ratings given to climate and each land quality/characteristic except the minimum rating.
508
Physical land suitability evaluation for specific cereal crops using GIS
Kalogirou method According to this method (Kalogirou, 2002), some fifteen land qualities or characteristics are distributed into five classdetermining factors which are then combined with a climatic rating to produce a land index and the final class of land suitability for each specific crop. Class-determining factors are variables that affect the performance of a land utilization type (LUT) on a land unit, serving as a basis for classifying the suitability of land for a given use. Many factors affect the performance of a land utilization type on a given land unit; in a suitability classification, some are ‘class-determining’ and others are not. Some factors affect a crop with its irrigation and management rather uniformly across all land units in the study area, or cause unimportant variations. In land evaluation, only the most important factors (those prospective classdetermining ones) need be assessed and can usually be progressively short-listed or aggregated into estimates of yields to evaluate the land suitability classes. Each classdetermining factor includes the following variables: Factor A is for determining soil physical properties (S) of soil texture/structure (var1), %Gravel volume (var2) and soil depth (var3). Factor B is for determining soil fertility and chemical properties (F) of pH (var4), %OC (var5), CEC (var6), %BS (var7), %CaCO3 (var8) and %Gypsum (var9). Factor C is for determining soil salinity and alkalinity (A) of EC (var10) and ESP (var11). Factor D is for determining topography (T) of %slope (var12) and erosion hazard (E) (var13). Factor E is for determining wetness (W) of flooding (var14) and drainage hazards (var15). Based on the number of variables and the scores assigned to individual land characteristics (variables), the final score given to each factor is calculated as follows: Score for class determining factor A = (var1 + var2 + var3)/3, Table 3
Score for class determining factor B = (var4 + var5 + var6 + var7 + var8 + var9)/6, Score for class determining factor C = (var10 + var11)/2, Score for class determining factor D = (var12 + var13) / 2, Score for class determining factor E = (var14 + var15) / 2. The score for climate factor (F) is the estimated climatic rating for each specific crop. This procedure is necessary for every land characteristic to contribute a different weight in the final land index. The land index is calculated by a modified equation of Kalogirou (2002) as follows: n Y
I¼ where, I is land index,
n Y
xi i¼1 , 100ðn – 1Þ
(4)
xi is multiplication of the scores and
i¼1
n is the number of class-determining factors.
Results and discussion Suitability is largely a matter of producing yield with relatively low inputs, and there are two stages in finding the land suited to a specific crop. The first stage focuses on being aware of the requirements of the crop, or alternatively what soil and site attributes adversely influence the crop. The second stage is to identify and delineate the land with the desirable attributes. In the present study, the specific soil and climate requirements for irrigated wheat, barley, grain maize and sorghum were determined based on Sys et al., (1991a, 1991b, 1993). There was an optimal climatic condition at the study area for barley and in a lower grade for wheat cultivation with climatic ratings of 86.16 to 82.13, which made this area highly suitable (S1 class) to moderately suitable (S2 class) for these crops, respectively (Tables 3 and 4).
Climatic requirements and characteristics for wheat, barley, grain maize and sorghum cultivation at the study area
Climatic characteristics Growing cycle date
Wheat
Barley
Maize
Sorghum
10 Oct.–10 Nov. 1 Apr.–10 Jul.
10 Oct.–10 Nov. 1 Apr.–15 Jul.
10 May–15 Sep.
15 May–20 Sep.
Mean temp. during growing cycle (°C)
20.58
19.59
25.40
25.40
Mean min. temp. during growing cycle (°C)
—
—
12.70
12.70
Mean max. temp. during growing cycle (°C)
—
—
—
38.10
Mean temp. at vegetative stage (°C)
16.26
15.52
—
—
Mean temp. at flowering stage (°C)
22.79
20.93
—
—
Mean temp. at ripening stage (°C)
27.01
26.39
—
—
Mean daily min. temp. in the coldest month (°C)
– 1.67
– 1.67
—
—
Mean daily max. temp. in the coldest month (°C)
8.15
8.15
—
— —
Relative humidity at developing stage (%)
—
—
30.80
Relative humidity at maturation stage (%)
—
—
34.39
—
Relative humidity during growing cycle (%)
—
—
—
30.29
* n/N at maturation stage
—
—
0.76
—
n/N during growing cycle
—
—
—
0.70
* n/N is the number of sunny hours to daylight hours.
Ali BAGHERZADEH and Mohammad Reza MANSOURI DANESHVAR Table 4
509
Climatic index, climatic rate and climatic class for each crop at the study area
Climatic characteristics
Wheat
Barley
Grain maize
Climatic index
72.73
77.21
41.90
48.43
Climatic rate
82.13
86.16
54.38
52.75
Climatic class
S2
S1
S3
S3
The low relative humidity at the development stage for grain maize and high mean maximum temperature during the growing cycle for sorghum were responsible for the marginal climate suitability (S3 class) of these crops, respectively (Tables 3 and 4). The most important land-limiting factors for irrigated wheat and barley at the study area are soil physical and fertility properties, while the climate and soil physical properties are the most responsible factors affecting the land suitability for grain maize and sorghum, respectively. Based on parametric Storie and square root methods, soil qualities/ characteristics of soil texture/structure, %gravel volume, %OC, CEC and pH affect the land suitability of irrigated wheat and barley, mainly resulting in the current unsuitability of the N1 class by the Storie method and the moderate Table 5
Sorghum
suitability of the S2 class to the marginal suitability of the S3 class by Kalogirou and square root methods (Tables 5 and 6). In most parts of the study area, climate, soil texture/ structure, %gravel volume, %OC and pH are the main limiting factors for irrigated grain maize and sorghum. The land suitability evaluation by different parametric methods indicated that most of the land units are classified into N2 (permanently unsuitable) and N1 (currently unsuitable) classes by the Storie and squire root methods and S3 by the Kalogirou method (Tables 7 and 8). According to the Kalogirou method, different land characteristics are distributed into five class-determining factors which contribute different weights of variables more logically than any of the other parametric methods in the final
Land index and land suitability classes and sub-classes based on parametric methods for irrigated barley Storie method
Profile No.
Square root method
Kalogirou method
Land index
Suitability class/subclass
Land index
Suitability class/subclass
Land index
1
9.68
N2s
12.47
N2s
47.79
Suitability class/subclass S3s
2
8.48
N2s
11.65
N2s
46.14
S3s
3
17.10
N1s
19.95
N1s
35.88
S3s
4
57.37
S2s
64.65
S2s
69.71
S2s
5
11.49
N2c
13.54
N1c
51.23
S2c
6
19.38
N1s
28.21
S3s
51.89
S2s
7
26.70
S3s
27.29
S3s
41.36
S3s
8
26.76
S3s
33.78
S3s
49.16
S3s
9
27.71
S3s
32.25
S3s
45.78
S3s
10
21.90
N1s
23.59
N1s
38.49
S3s
11
22.66
N1s
28.40
S3s
44.56
S3s
12
25.87
S3s
30.26
S3s
44.17
S3s
13
18.46
N1s
29.27
S3s
48.50
S3s
14
31.64
S3s
39.05
S3s
53.10
S2s
15
20.52
N1s
29.26
S3s
51.38
S2s
16
22.00
N1s
31.25
S3s
52.88
S2s
17
17.71
N1s
27.38
S3s
48.66
S3s
18
17.50
N1s
22.57
N1s
39.44
S3s
19
22.69
N1s
28.81
S3s
52.74
S2s
20
21.55
N1s
29.03
S3s
49.85
S3s
21
33.69
S3s
44.51
S3s
61.64
S2s
22
32.21
S3s
45.05
S3s
63.37
S2s
23
38.00
S3s
46.47
S3s
58.35
S2s
24
21.80
N1s
31.76
S3s
45.15
S3s
25
19.95
N1s
27.36
S3s
49.34
S3s
26
22.52
N1s
32.25
S3s
52.54
S2s
27
24.70
N1s
26.80
S3s
41.50
S3s
28
37.71
S3s
47.79
S3s
60.53
S2s
510 Table 6
Physical land suitability evaluation for specific cereal crops using GIS Land index and land suitability classes and sub-classes based on parametric methods for irrigated wheat. Storie method
Profile No.
Square root method
Kalogirou method
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
1
4.53
N2f
4.61
N2 f
42.84
S3 f
2
3.36
N2 f
3.36
N2 f
40.99
S3 f
3
14.61
N1s
18.44
N1s
33.53
S3s
4
48.43
S3c
57.16
S2c
65.15
S2c
5
2.99
N2 f
2.07
N2 f
45.10
S3 f
6
16.76
N1s
26.23
S3s
48.57
S3s
7
20.20
N1s
23.74
N1s
38.06
S3s
8
22.49
N1s
30.97
S3s
45.84
S3s
9
23.79
N1s
29.88
S3s
42.86
S3s
10
18.94
N1s
21.94
N1s
36.06
S3s
11
19.27
N1s
26.19
S3s
41.62
S3s
12
22.04
N1s
27.94
S3s
41.30
S3s
13
15.88
N1s
27.15
S3s
45.37
S3s
14
26.71
S3s
35.88
S3s
49.57
S3s
15
17.17
N1s
26.76
S3s
47.80
S3s
16
18.40
N1s
28.58
S3s
49.21
S3s
17
14.70
N1s
24.95
N1s
45.24
S3s
18
15.13
N1s
20.99
N1s
36.93
S3s
19
19.25
N1s
26.54
S3s
49.21
S3s
20
18.19
N1s
26.67
S3s
46.48
S3s
21
28.06
S3c
40.62
S3c
57.38
S2c
22
27.19
S3c
41.39
S3c
56.33
S2c
23
31.72
S3s
42.45
S3s
54.38
S2s
24
18.04
N1s
28.89
S3s
41.98
S3s
25
16.84
N1s
25.14
S3s
45.99
S3s
26
16.16
N1s
26.13
S3s
47.89
S3s
27
21.10
N1s
24.77
N1s
38.81
S3s
28
32.22
S3s
44.17
S3s
56.62
S2s
Table 7
Land index and land suitability classes and sub-classes based on parametric methods for irrigated grain maize Storie method
Profile No.
Square root method
Kalogirou method
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
1
3.11
N2c
3.82
N2c
28.33
S3c
2
2.35
N2c
2.81
N2c
27.38
S3c
3
8.74
N2s
14.26
N1s
21.83
N1s
4
33.35
S3c
31.40
S3c
43.24
S3c
5
1.97
N2c
1.68
N2c
29.60
S3c
6
8.23
N2c
15.60
N1c
30.50
S3c
7
11.53
N2s
17.93
N1s
24.79
N1s
8
15.37
N1c
21.32
N1c
30.31
S3c
9
13.32
N1c
19.85
N1c
27.67
S3c
10
9.54
N2s
15.57
N1s
22.99
N1s
11
12.20
N2c
19.00
N1c
27.29
S3c
12
13.55
N1c
20.02
N1c
26.96
S3c
13
8.43
N2c
15.79
N1c
29.03
S3c
14
18.26
N1c
23.24
N1c
32.80
S3c
15
11.18
N2c
18.19
N1c
31.20
S3c
16
11.53
N2c
18.47
N1c
31.39
S3c
17
7.57
N2c
14.96
N1c
28.76
S3c
18
7.57
N2s
14.85
N1s
23.49
N1s
Ali BAGHERZADEH and Mohammad Reza MANSOURI DANESHVAR
511 (Continued)
Storie method
Profile No.
Square root method
Kalogirou method
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
19
12.59
N1c
19.30
N1c
32.47
S3c
20
12.50
N1c
19.23
N1c
30.85
S3c
21
18.86
N1c
23.62
N1c
37.66
S3c
22
18.20
N1c
23.20
N1c
37.20
S3c
23
21.51
N1c
25.22
S3c
35.74
S3c
24
12.48
N2c
19.21
N1c
27.84
S3c
25
11.43
N2c
18.39
N1c
30.35
S3c
26
9.67
N2c
16.91
N1c
31.34
S3c
27
12.62
N1s
19.16
N1s
25.28
S3s
28
18.71
N1c
23.52
N1c
36.36
S3c
Table 8
Land index and land suitability classes and sub-classes based on parametric methods for irrigated sorghum Storie method
Profile No.
Square root method
Kalogirou method
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
Land index
Suitability class/ subclass
1
3.08
N2c
3.80
N2c
27.75
S3c
2
2.31
N2c
2.78
N2c
26.70
S3c
3
10.11
N2s
15.34
N1s
21.81
N1s
4
33.29
S3c
30.44
S3c
42.28
S3c
5
1.98
N2c
1.69
N2c
29.16
S3c
6
11.61
N2c
17.97
N1c
31.55
S3c
7
14.07
N1c
19.78
N1c
24.77
N1c
8
15.42
N1c
20.71
N1c
29.76
S3c
9
16.50
N1c
21.43
N1c
27.87
S3c
10
13.19
N1s
18.31
N1s
23.47
N1s
11
13.28
N1c
19.22
N1c
27.05
S3c
12
15.21
N1c
20.57
N1c
26.82
S3c
13
10.82
N2c
17.35
N1c
29.42
S3c
14
18.26
N1c
22.54
N1c
32.10
S3c
15
11.26
N2c
17.70
N1c
30.75
S3c
16
12.24
N2c
18.45
N1c
31.73
S3c
17
10.17
N2c
16.82
N1c
29.37
S3c
18
10.47
N2c
17.07
N1c
24.03
N1c
19
19.22
N1c
13.27
N1c
32.00
S3c
20
12.51
N1c
18.66
N1c
30.21
S3c
21
18.81
N1c
22.88
N1c
37.00
S3c
22
18.24
N1c
22.53
N1c
36.44
S3c
23
21.44
N1c
24.43
N1c
35.08
S3c
24
12.18
N2c
18.41
N1c
27.13
S3c
25
11.46
N2c
17.86
N1c
29.75
S3c
26
11.26
N2c
17.70
N1c
31.21
S3c
27
14.59
N1c
20.15
N1c
25.23
S3c
28
22.03
N1c
24.76
N1c
36.60
S3c
land suitability class. Based on this method, soil physical and fertility/chemical properties are dominant limiting factors for irrigated wheat and barley, while the climate criterion (climatic rating) is the dominant limiting factor affecting the land suitability for irrigated grain maize and sorghum. The
corresponding land suitability classes with this method were S3 (marginally suitable) and S2 (moderately suitable) for irrigated wheat and barley, while the dominant suitability classes were S3 (marginally suitable) and N1 (currently unsuitable) for grain maize and sorghum (Tables 5–8).
512
Physical land suitability evaluation for specific cereal crops using GIS
Implementing land suitability evaluation models in the geographic information system (GIS) enables an analysis that is more relevant to decision-makers than the original basic data in planning land developments. The GIS interpolation functions help manage the spatial data and visualize the results. Hence, interpolating the soil profile, dataset were used in the ArcGIS ver.9.3 for preparing the final land suitability evaluation maps (Figs. 6–9).
Figure 8
Figure 6
Land suitability map for irrigated grain maize cultivation.
Land suitability map for irrigated barley cultivation.
Figure 9 Land suitability map for irrigated sorghum cultivation. Table 9 Land index and land suitability classes for irrigated barley and wheat crops by parametric Kalogirou method at the study area Crops
Land index
Class
Area (km2)
Percent of total study area
Barley
50–75
S2
3984
64.99
40–50
S3
2146
35.01
50–75
S2
2488
40.59
40–50
S3
3642
59.41
Wheat
Figure 7
Land suitability map for irrigated wheat cultivation.
Additionally, the results in figures 6 and 7 reveal that about 40.6% to 65% of the study area has moderate suitability (S2 class) and 59.4% to 35% has marginal suitability (S3 class) for irrigated wheat and barley, respectively (Table 9). Accordingly, the produced maps on figures 8 and 9 show that about 94.6% to 94.8% of the study area has marginal suitability (S3 class) and 5.4% to 5.2% has currently no suitability (N1 class) for grain maize and sorghum, respectively (Table 10). It was indicated that all land units suitable for irrigated wheat cultivation overlap with those of barley and the same
Table 10 Land index and land suitability classes for irrigated grain maize and sorghum crops by parametric Kalogirou method at the study area Crops
Land index
Class
Area (km2)
Percent of total study area
Grain maize
35–50
S3
1620
26.43
25–35
S3
4178
68.17
20–25
N1
331
5.40
35–50
S3
1359
22.17
25–35
S3
4453
72.65
20–25
N1
317
5.17
Sorghum
Ali BAGHERZADEH and Mohammad Reza MANSOURI DANESHVAR
limiting factors were observed in overlapping land units covered by irrigated grain maize with those of sorghum.
Conclusion Physical land suitability evaluation is land efficiency assessment for a specific utilization type. The yield ratings and socio-economic factors were not considered in this evaluation, and the results were determined by qualitative terms. This study surveyed land and climate characteristics based on Sys et al. (1991a, 1991b, 1993) to produce a qualitative land suitability evaluation for specific cereal crops at Mashhad Plain, Northeast of Iran. Based on parametric approaches, irrigated wheat and barley cultivations in terms of their land suitability were classified into S2, S3 and N1 classes, while irrigated grain maize and sorghum were categorized into S3 and N2 classes, revealing that the area of interest was mostly unsuitable for these cultivations. Hence, for an average high yield to be achieved, high input will meet the requirement for the increasing costs of crop production, whereas the land is capable of producing an average yield for irrigated wheat and barley crops after an average input.
Acknowledgements We thank Islamic Azad University-Mashhad branch for their support of the project. Thanks are also given to one anonymous reviewer for generous suggestions on data analyses and interpretations.
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